Patch and Position Embeddings: As it can be seen from Fig. REF , the segmented CP data \(\textbf {X}_u\)  is split into \(N\)  sequence of non-overlapping patches with the fixed-size of \((S\times S)\) , where the total number of patches is \(N = w / S\) . After this step, each patch is flattened into a vector \({\mathbf {x}}^{p}_{u,_{j}} \in \mathbb {R}^{S^{2}}\)  for (\(1 \le j \le N\) ). To embed vector \({\mathbf {x}}^{p}_{u,_{j}} \in \mathbb {R}^{S^{2}}\)  into the model's dimension \(d\) , a linear projection \(\mathbf {E}\in \mathbb {R}^{S^{2}\times d}\)  is used, which is shared among all patches, where the output of this projection is referred to as the patch embeddings. We append a learnable embedding token \({\mathbf {x}}^{cls}\)  to the beginning of the sequence of embedded patches [1]}. Finally, the position embeddings \(\mathbf {E}^{pos}\in \mathbb {R}^{(N+ 1)\times d}\) , is added to the patch embeddings to explicitly encode the order of the input sequence. The output of the patch and position embeddings \(\mathbf {Z}_0\)  is given by
\(\mathbf {Z}_0 = [{\mathbf {x}}^{cls}; {\mathbf {x}}^{p}_{u,1}\mathbf {E}; {\mathbf {x}}^{p}_{u,2}\mathbf {E};\dots ; {\mathbf {x}}^{p}_{u,N}\mathbf {E}] + \mathbf {E}^{pos}. \) 
